In the field of oncology it is important to be able to discriminate tumor tissue from normal tissue. Golden standard is to inspect tissue at the pathology department after a biopsy or after surgical resection. A drawback of this current way of working is that real time feedback during the procedure of taking a biopsy or performing the surgical resection is missing. Incorporating optical fibers in a biopsy needle would be for instance of great interest to physicians to use as a feedback device during their clinical interventions. Various optical methods can be employed, e.g., diffuse reflectance spectroscopy (DRS) and autofluorescence measurement as the techniques that are most commonly investigated. The spectroscopic data are used to classify the different tissue types using standard classification methods. Usually a database is built for classification with spectra from many patients and is used for regression for new measurements to predict the class to which they belong.
A problem with classifying tissue type of an individual patient using such a database is that inter-patient variance hampers tissue discrimination. It has been demonstrated that in breast tissue the fat content increases whereas glandular tissue decreases with age. Therefore, a very large standard deviation in fat and collagen exists due to a wide range of age. The sensitivity and specificity of the methods using spectroscopic point measurements are moderate (50-85%) and results strongly vary in the literature. The moderate sensitivity makes the approach not optimal for an individual patient approach.
FIG. 2 shows inter-patient variation in a score plot of partial least squares discriminant analysis (PLS-DA) prediction scores of breast tissue classification of all patients. More specifically, FIG. 2 shows a result of an all-patient PLS-DA analysis of diffuse reflectance spectroscopic measurements on ex-vivo human breast tissues samples. From FIG. 2 it can be seen that inter-patient variation is an issue when discriminating for example fibroadenoma (FA, benign) from gland (G, normal) tissue, the gland tissue represented by symbols “⋄” which are scattered and mix up with other tissue type measurements. In FIG. 2, other tissue types are fibroadenoma (FA) represented by symbols “+”, adenocarcinoma (A) represented by symbols “x”, ductal carcinoma in situ (DCIS) represented by symbols “◯”, and fat (F) represented by symbols “□”.
Thus, a discrimination would be desirable, which is tuned to the individual patient, where spectroscopic measurements at different positions such as in normal and/or benign and malignant tissues in the individual patient are obtained, if possible, a classification model can be provided using individual patient data and a priori spectroscopic and clinical patient knowledge, and tissue types are classified for the patient without hindrance of inter-patient variance.
However, it is in this case not trivial how to classify individual patient spectroscopic with high sensitivity and specificity. Neither is it straightforward to determine whether the data collected in the above step one is malignant tissue (chicken-egg problem). Furthermore, from the above it is not clear how this approach would fit in the workflow of a clinician. Building first a database for an individual patient and then using this for further classification is time consuming compared to using a pre-collected database of various patients and performing classification based on this database. In this case the database need not to be built up during the intervention but hampers from the above mentioned inter-patient variations which results in low sensitivity. The sensitivity and specificity of conventional methods using spectroscopic point measurements usually are moderate and also strongly vary in the literature. The moderate sensitivity makes the method not ideal for an individual patient. To overcome this problem a method is needed that is tuned to the individual patient.
The US 2006/0173352 A1 discloses a method for detecting pre-disease transformations in tissue of mammals, which comprises illuminating a volume of selected tissue with light having wavelengths covering a pre-selected spectral range, detecting light transmitted through, or reflected from, the volume of the selected tissue, and obtaining a spectrum of the detected light. The spectrum of detected light is then represented by one or more basis spectral components. The associated scalar coefficient of the each of the basis spectral components is correlated with a pre-selected property of the selected tissue known to be indicative of susceptibility of the tissue for the pre-selected disease to obtain the susceptibility for the mammal to developing the pre-selected disease. Statistical significance for principal component analysis prediction was established using high density measure (HDM) as it is preferable over increased low density measure (LDM) both are similar sensitivity and specificity.
Furthermore, the US 2009/0317856 A1 disclosed multimodal optical spectroscopy systems and methods, in which a spectroscopic event is produced to obtain spectroscopic response data from biological tissue and compare the response data with preset criteria configured to correlate the measured response data and the most probable attributes of the tissue, thus facilitating classification of the tissue based on those attributes for subsequent biopsy or remedial measures as necessary. Tissue classification algorithms were developed to employ reflectance and fluorescene spectroscopy for differentiating between human pancreatic adenocarcinoma and pancreatitis tissue. Linear Discriminant Analysis was used to classify the test data into adenocarcinoma or not adenocarcinoma based on the fit-coefficient values for all or a subset of four principal component values.
Additionally, the US 2007/0167836 A1 discloses a system for multi modal spectroscopy where Raman spectroscopy is combined with fluorescence, reluctance and optionally light scattering spectroscopy. An algorithm for diagnosis of vulnerable plaque incorporates contributions from metabolically active, potential scatterers like foam cells as well as non-metabolically active plaque constituents like the necrotic core. The key spectroscopic parameters obtained from intrinsic fluorescence signature, diffuse reflectance spectroscopy and Raman spectroscopies are categorized as yes/no results based on threshold values.
Further, the US 2008/0221455 A1 discloses methods and devices for characterizing tissue in vivo, e.g., in walls of blood vessels, to determine whether the tissue is healthy or diseased. Results are displayed with or without thresholds. A single threshold may not be optimal for all patients because of inter-patient variation. In other words, the reflected radiation in one patient may not mean the same thing in another patient. Displaying the data directly enables the operator to decide upon a patient-specific threshold after taking individual patient considerations into account.
Moreover, the US 2009/0002702 A1 discloses a system to provide a diagnosis of the renal disease state of a test renal sample. A database containing a plurality of reference Raman data sets is provided where each reference Raman data set has an associated known renal sample and an associated known renal disease state. A test renal sample is irradiated with substantially monochromatic light to generate scatted photons resulting in a test Raman data set. The test Raman data set is compared to the plurality of reference Raman data sets using a chemometric technique. Based on the comparison, a diagnosis of a renal disease state of the test renal sample is provided and the data set is classified into predetermined cases.
Finally, the US 2008/0221457 A1 discloses methods and apparatus for classifying tissue use features of Raman spectra and background fluorescent spectra. The spectra may be acquired in the near-infrared wavelengths. Principal component analysis and linear discriminant analysis of reference spectra may be used to obtain a classification function that accepts features of the Raman and background fluorescence spectra for test tissue and yields an indication as to the likelihood that the test tissue is abnormal. The methods and apparatus may be applied to screening for skin cancers or other diseases. Difference as well us ine peak positions and bandwidths of the two melanin Raman bands may be included as features and used for non-invasive melanoma detection.